{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ntu_food_cnn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO1Qv7y6lyOwMHDT0W2HxLo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bradleetw/Colab_NTU_homework/blob/master/Ntu_food_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlqNKvjS_f6I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a2ff4f1f-ed67-42f8-f5c1-9507bff69309"
      },
      "source": [
        "!gdown --id '19CzXudqN58R3D-1G8KeFWk8UDQwlb8is' --output food-11.zip # 下載資料集\n",
        "!unzip food-11.zip # 解壓縮"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLEZUjvT_rBR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import torch\n",
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4_EBFmyBLbv",
        "colab_type": "text"
      },
      "source": [
        "## Read Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcCdHcflBl4t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from pathlib import Path\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TdsmKQQCqZR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cur_path = Path(os.getcwd())\n",
        "dataset_name = './food-11'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjzpdDrpArmZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a772adb6-6829-485d-c9b3-abbb6d6185c0"
      },
      "source": [
        "folder_list = [\"training\", \"validation\", \"testing\"]\n",
        "ret_item = list()\n",
        "for folder in folder_list:\n",
        "    folder_path = cur_path / dataset_name / folder\n",
        "    image_files = sorted(os.listdir(folder_path))\n",
        "    x = np.zeros((len(image_files), 128, 128, 3), dtype=np.uint8)\n",
        "    y = None\n",
        "    if folder != \"testing\":\n",
        "        y = np.zeros((len(image_files)), dtype=np.uint8)\n",
        "    for idx, fitem in enumerate(image_files):\n",
        "        # img = cv2.imread(os.path.join(folder_path, fitem))\n",
        "        img = cv2.imread(str(folder_path / fitem))\n",
        "        x[idx, :, :] = cv2.resize(img, (128, 128))\n",
        "        if folder != \"testing\":\n",
        "            y[idx] = int(fitem.split(\"_\")[0])\n",
        "        if idx % 100 == 0:\n",
        "            print(f\"{idx}, {folder_path / fitem}\")\n",
        "    ret_item.append((x, y))\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1lmSHDyO03E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_transform = transforms.Compose([\n",
        "    transforms.ToPILImage(),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomRotation(15),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "test_transform = transforms.Compose([\n",
        "    transforms.ToPILImage(),\n",
        "    transforms.ToTensor(),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XCxqa7_PsQI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ImageDataset(Dataset):\n",
        "    def __init__(self, x, y=None, transform=None):\n",
        "        self._x = x\n",
        "        self._y = y\n",
        "        if y is not None:\n",
        "            self._y = torch.LongTensor(y)\n",
        "        self._transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self._x)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        _X = self._x[index]\n",
        "        if self._transform is not None:\n",
        "            _X = self._transform(_X)\n",
        "        if self._y is None:\n",
        "            return _X\n",
        "        return _X, self._y[index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MHVUGMkPxkX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert_to_ImageDataset(x, y=None, transform=None):\n",
        "    return ImageDataset(x, y, transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHwo3bgjP0VX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "((x_train, y_train), (x_valid, y_valid), (x_test, _)) = ret_item"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICsgdRvgQyGn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ret_dict = {}\n",
        "ret_dict['train_set'] = convert_to_ImageDataset(x_train, y_train, train_transform)\n",
        "ret_dict['valid_set'] = convert_to_ImageDataset(x_valid, y_valid, test_transform)\n",
        "ret_dict['test_set'] = convert_to_ImageDataset(x_test, transform=test_transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4y5FQ45RFcf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VTLDZcyRNqa",
        "colab_type": "text"
      },
      "source": [
        "## Build Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FswLWGLBRQHs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SemiAlexNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SemiAlexNet, self).__init__()\n",
        "        self.convs = nn.Sequential(\n",
        "            nn.Conv2d(3, 96, kernel_size=8, stride=4),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(3, stride=2),\n",
        "            nn.Conv2d(96, 256, kernel_size=5, stride=1, padding=2),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(3, stride=2),\n",
        "            nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(384, 384, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(3, stride=2),\n",
        "        )\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(3*3*256, 1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 11),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.convs(x)\n",
        "        # print(out.size())\n",
        "        # out = out.view(1, -1)\n",
        "        out = out.view(out.size()[0], -1)\n",
        "        # print(out.size())\n",
        "        out = self.fc(out)\n",
        "        return out\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m97gNnJRse1",
        "colab_type": "text"
      },
      "source": [
        "## Training process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqmMkUDjSHPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bs = 64\n",
        "lr = 0.001\n",
        "epochs = 30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYZLigkjRjoe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_func = nn.CrossEntropyLoss()\n",
        "model = SemiAlexNet().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7L4ea-6R96c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "opt = torch.optim.Adam(model.parameters(), lr=lr)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmTw9sQ7SMFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ret_dict\n",
        "train_dl = DataLoader(data['train_set'], batch_size=bs, shuffle=True)\n",
        "valid_dl = DataLoader(data['valid_set'], batch_size=bs*2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoGnAVL_Sc_F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "855aa3ad-56b2-44bd-83e5-e92e37a8f2b7"
      },
      "source": [
        "for epoch in range(epochs):\n",
        "    epoch_start_time = time.time()\n",
        "    train_acc = 0.0\n",
        "    train_loss = 0.0\n",
        "    val_acc = 0.0\n",
        "    val_loss = 0.0\n",
        "    model.train()\n",
        "    for xb, yb in train_dl:\n",
        "        opt.zero_grad()\n",
        "        train_pred = model(xb.cuda())\n",
        "        batch_loss = loss_func(train_pred, yb.cuda())\n",
        "        batch_loss.backward()\n",
        "        opt.step()\n",
        "        train_acc += np.sum(np.argmax(train_pred.cpu().data.numpy(),\n",
        "                                        axis=1) == yb.numpy())\n",
        "        train_loss += batch_loss.item()\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for xv, yv in valid_dl:\n",
        "            valid_pred = model(xv.cuda())\n",
        "            batch_loss = loss_func(valid_pred, yv.cuda())\n",
        "            val_acc += np.sum(np.argmax(valid_pred.cpu().data.numpy(),\n",
        "                                        axis=1) == yv.numpy())\n",
        "            val_loss += batch_loss.item()\n",
        "\n",
        "        print('[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f | Val Acc: %3.6f loss: %3.6f' %\n",
        "                (epoch + 1, epochs, time.time()-epoch_start_time,\n",
        "                train_acc/data['train_set'].__len__(), train_loss/data['train_set'].__len__(), val_acc/data['valid_set'].__len__(), val_loss/data['valid_set'].__len__()))\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[001/030] 10.11 sec(s) Train Acc: 0.543787 Loss: 0.020590 | Val Acc: 0.496210 loss: 0.011965\n",
            "[002/030] 10.17 sec(s) Train Acc: 0.542570 Loss: 0.020138 | Val Acc: 0.468805 loss: 0.012086\n",
            "[003/030] 10.08 sec(s) Train Acc: 0.562639 Loss: 0.019697 | Val Acc: 0.490087 loss: 0.012153\n",
            "[004/030] 10.17 sec(s) Train Acc: 0.579870 Loss: 0.019125 | Val Acc: 0.494169 loss: 0.012204\n",
            "[005/030] 10.06 sec(s) Train Acc: 0.594973 Loss: 0.018385 | Val Acc: 0.488047 loss: 0.012641\n",
            "[006/030] 10.17 sec(s) Train Acc: 0.596290 Loss: 0.018176 | Val Acc: 0.489213 loss: 0.012134\n",
            "[007/030] 10.15 sec(s) Train Acc: 0.612508 Loss: 0.017329 | Val Acc: 0.490087 loss: 0.012399\n",
            "[008/030] 10.17 sec(s) Train Acc: 0.622238 Loss: 0.016706 | Val Acc: 0.451020 loss: 0.013670\n",
            "[009/030] 10.05 sec(s) Train Acc: 0.638759 Loss: 0.016414 | Val Acc: 0.490379 loss: 0.012543\n",
            "[010/030] 10.14 sec(s) Train Acc: 0.649503 Loss: 0.015704 | Val Acc: 0.505248 loss: 0.012969\n",
            "[011/030] 10.10 sec(s) Train Acc: 0.658524 Loss: 0.015164 | Val Acc: 0.498251 loss: 0.013101\n",
            "[012/030] 10.16 sec(s) Train Acc: 0.678289 Loss: 0.014413 | Val Acc: 0.489796 loss: 0.013951\n",
            "[013/030] 10.14 sec(s) Train Acc: 0.689337 Loss: 0.014004 | Val Acc: 0.485423 loss: 0.014251\n",
            "[014/030] 10.13 sec(s) Train Acc: 0.706061 Loss: 0.013292 | Val Acc: 0.490379 loss: 0.013938\n",
            "[015/030] 10.10 sec(s) Train Acc: 0.730590 Loss: 0.012311 | Val Acc: 0.490962 loss: 0.014389\n",
            "[016/030] 10.12 sec(s) Train Acc: 0.724711 Loss: 0.012291 | Val Acc: 0.486006 loss: 0.014965\n",
            "[017/030] 10.08 sec(s) Train Acc: 0.735658 Loss: 0.011884 | Val Acc: 0.485714 loss: 0.014722\n",
            "[018/030] 10.06 sec(s) Train Acc: 0.753497 Loss: 0.011233 | Val Acc: 0.497376 loss: 0.014989\n",
            "[019/030] 10.06 sec(s) Train Acc: 0.776100 Loss: 0.010284 | Val Acc: 0.500000 loss: 0.015250\n",
            "[020/030] 10.19 sec(s) Train Acc: 0.778938 Loss: 0.010418 | Val Acc: 0.475219 loss: 0.016157\n",
            "[021/030] 10.02 sec(s) Train Acc: 0.782587 Loss: 0.009888 | Val Acc: 0.480466 loss: 0.016950\n",
            "[022/030] 10.08 sec(s) Train Acc: 0.792722 Loss: 0.009464 | Val Acc: 0.478134 loss: 0.017267\n",
            "[023/030] 10.08 sec(s) Train Acc: 0.800932 Loss: 0.008919 | Val Acc: 0.485714 loss: 0.015980\n",
            "[024/030] 10.12 sec(s) Train Acc: 0.824752 Loss: 0.008017 | Val Acc: 0.497668 loss: 0.018678\n",
            "[025/030] 10.08 sec(s) Train Acc: 0.817454 Loss: 0.008443 | Val Acc: 0.497376 loss: 0.017539\n",
            "[026/030] 10.06 sec(s) Train Acc: 0.823941 Loss: 0.007934 | Val Acc: 0.477843 loss: 0.017996\n",
            "[027/030] 10.07 sec(s) Train Acc: 0.826373 Loss: 0.007964 | Val Acc: 0.482507 loss: 0.019124\n",
            "[028/030] 10.15 sec(s) Train Acc: 0.842793 Loss: 0.007304 | Val Acc: 0.493003 loss: 0.019460\n",
            "[029/030] 10.11 sec(s) Train Acc: 0.856375 Loss: 0.006622 | Val Acc: 0.477259 loss: 0.020993\n",
            "[030/030] 10.12 sec(s) Train Acc: 0.849483 Loss: 0.007045 | Val Acc: 0.481633 loss: 0.019174\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RngHT0aUia95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_val_x = np.concatenate((x_train, x_valid), axis=0)\n",
        "train_val_y = np.concatenate((y_train, y_valid), axis=0)\n",
        "train_val_set = convert_to_ImageDataset(train_val_x, train_val_y, train_transform)\n",
        "train_val_loader = DataLoader(train_val_set, batch_size=bs, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3_Q-iN2ixjb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "5b3bd617-7783-411c-81b4-40e0578d9bcd"
      },
      "source": [
        "for epoch in range(epochs):\n",
        "    epoch_start_time = time.time()\n",
        "    train_acc = 0.0\n",
        "    train_loss = 0.0\n",
        "    model.train()\n",
        "    for xb, yb in train_val_loader:\n",
        "        opt.zero_grad()\n",
        "        train_pred = model(xb.cuda())\n",
        "        batch_loss = loss_func(train_pred, yb.cuda())\n",
        "        batch_loss.backward()\n",
        "        opt.step()\n",
        "        train_acc += np.sum(np.argmax(train_pred.cpu().data.numpy(),\n",
        "                                        axis=1) == yb.numpy())\n",
        "        train_loss += batch_loss.item()\n",
        "    print('[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f' % \\\n",
        "      (epoch + 1, epochs, time.time()-epoch_start_time, \\\n",
        "      train_acc/train_val_set.__len__(), train_loss/train_val_set.__len__()))        "
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[001/030] 11.53 sec(s) Train Acc: 0.773090 Loss: 0.011520\n",
            "[002/030] 11.56 sec(s) Train Acc: 0.781664 Loss: 0.010674\n",
            "[003/030] 11.46 sec(s) Train Acc: 0.790539 Loss: 0.010055\n",
            "[004/030] 11.50 sec(s) Train Acc: 0.795803 Loss: 0.009760\n",
            "[005/030] 11.46 sec(s) Train Acc: 0.799564 Loss: 0.009663\n",
            "[006/030] 11.57 sec(s) Train Acc: 0.814606 Loss: 0.008882\n",
            "[007/030] 11.51 sec(s) Train Acc: 0.817765 Loss: 0.008534\n",
            "[008/030] 11.50 sec(s) Train Acc: 0.823406 Loss: 0.008381\n",
            "[009/030] 11.47 sec(s) Train Acc: 0.827241 Loss: 0.007957\n",
            "[010/030] 11.48 sec(s) Train Acc: 0.831227 Loss: 0.007937\n",
            "[011/030] 11.49 sec(s) Train Acc: 0.839651 Loss: 0.007569\n",
            "[012/030] 11.52 sec(s) Train Acc: 0.854768 Loss: 0.006924\n",
            "[013/030] 11.53 sec(s) Train Acc: 0.851760 Loss: 0.006874\n",
            "[014/030] 11.56 sec(s) Train Acc: 0.862891 Loss: 0.006411\n",
            "[015/030] 11.43 sec(s) Train Acc: 0.854768 Loss: 0.006880\n",
            "[016/030] 11.48 sec(s) Train Acc: 0.866050 Loss: 0.006321\n",
            "[017/030] 11.51 sec(s) Train Acc: 0.869961 Loss: 0.006185\n",
            "[018/030] 11.53 sec(s) Train Acc: 0.873947 Loss: 0.005918\n",
            "[019/030] 11.51 sec(s) Train Acc: 0.871089 Loss: 0.006159\n",
            "[020/030] 11.67 sec(s) Train Acc: 0.871465 Loss: 0.006089\n",
            "[021/030] 11.53 sec(s) Train Acc: 0.883048 Loss: 0.005437\n",
            "[022/030] 11.50 sec(s) Train Acc: 0.887786 Loss: 0.005397\n",
            "[023/030] 11.52 sec(s) Train Acc: 0.887711 Loss: 0.005377\n",
            "[024/030] 11.52 sec(s) Train Acc: 0.900346 Loss: 0.004940\n",
            "[025/030] 11.50 sec(s) Train Acc: 0.886658 Loss: 0.005401\n",
            "[026/030] 11.50 sec(s) Train Acc: 0.896360 Loss: 0.004907\n",
            "[027/030] 11.49 sec(s) Train Acc: 0.900496 Loss: 0.004611\n",
            "[028/030] 11.60 sec(s) Train Acc: 0.904407 Loss: 0.004662\n",
            "[029/030] 11.48 sec(s) Train Acc: 0.898390 Loss: 0.004957\n",
            "[030/030] 11.45 sec(s) Train Acc: 0.896510 Loss: 0.004901\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvTBha9kl4Jy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_loader = DataLoader(ret_dict['test_set'], batch_size=bs, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lt7HXnlSnz7C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.eval()\n",
        "prediction = []\n",
        "with torch.no_grad():\n",
        "    for i, data in enumerate(test_loader):\n",
        "        test_pred = model(data.cuda())\n",
        "        test_label = np.argmax(test_pred.cpu().data.numpy(), axis=1)\n",
        "        for y in test_label:\n",
        "            prediction.append(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doUOkzK3n-hN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(\"predict.csv\", 'w') as f:\n",
        "    f.write('Id,Category\\n')\n",
        "    for i, y in  enumerate(prediction):\n",
        "        f.write('{},{}\\n'.format(i, y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ym5O3kF1pPmC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "saved_path = cur_path / \"semialexnet.pch\"\n",
        "torch.save({\n",
        "            'epoch': epochs,\n",
        "            'model_state_dict': model.state_dict(),\n",
        "            'optimizer_state_dict': opt.state_dict(),\n",
        "            }, saved_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YeDxDAFoHNl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "6b273d6b-0df7-4711-d926-5522e8859a9a"
      },
      "source": [
        "%ll"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1213412\n",
            "drwxr-xr-x 5 root       4096 May 18 12:02 \u001b[0m\u001b[01;34mfood-11\u001b[0m/\n",
            "-rw-r--r-- 1 root 1163028761 May 18 10:36 food-11.zip\n",
            "-rw-r--r-- 1 root      22594 May 18 11:51 predict.csv\n",
            "drwxr-xr-x 1 root       4096 May 13 16:29 \u001b[01;34msample_data\u001b[0m/\n",
            "-rw-r--r-- 1 root   79465943 May 18 12:03 semialexnet.pch\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQzCZaXioSmq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('predict.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHMBfThQop-w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "6ae2e591-56bd-4a76-85fa-87d08247c9cc"
      },
      "source": [
        "files.download('semialexnet.pch')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 51346, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 320, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 351, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 364, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 724, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 803, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFJsyNjrq1Ha",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "64822ce8-9e66-4ba0-f9f5-a7eedfaf8d67"
      },
      "source": [
        "from google.colab import drive\n",
        "# 將自己的雲端硬碟掛載上去\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zjcNCQ9sp56",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "abae27d3-d514-45b6-fe8c-4c8f52cdbd05"
      },
      "source": [
        "%ls"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mfood-11\u001b[0m/  food-11.zip  \u001b[01;34mgdrive\u001b[0m/  predict.csv  \u001b[01;34msample_data\u001b[0m/  semialexnet.pch\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYeNBetOs6Xi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9e48c82d-2c85-4c02-bc7c-831165545949"
      },
      "source": [
        "%ls gdrive/'My Drive'/ai"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " \u001b[0m\u001b[01;34mPaper\u001b[0m/           \"The Practitioner's Guide with TF High Level APIs.gslides\"\n",
            " semialexnet.pch\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCdcy7-Cs-AF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cp semialexnet.pch gdrive/'My Drive'/ai"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lm2ce6NotQjM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_num_params(model):\n",
        "    pp = 0\n",
        "    for p in model.parameters():\n",
        "        nn = 1\n",
        "        for s in p.size():\n",
        "            nn = nn * s\n",
        "        pp += nn\n",
        "    return pp\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nntrpRdUtjzC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "11d65cba-23ef-4a3c-d4ee-93f6463f4fef"
      },
      "source": [
        "get_num_params(model)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6621547"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sg8Qq-8ntmby",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d9c464de-394e-4309-93cc-0349c1437e38"
      },
      "source": [
        "opt.state_dict()['param_groups']"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'amsgrad': False,\n",
              "  'betas': (0.9, 0.999),\n",
              "  'eps': 1e-08,\n",
              "  'lr': 0.001,\n",
              "  'params': [139749081028792,\n",
              "   139749230032144,\n",
              "   139749230354368,\n",
              "   139749235621104,\n",
              "   139749081180704,\n",
              "   139749081180560,\n",
              "   139749081180416,\n",
              "   139749081178256,\n",
              "   139749081179264,\n",
              "   139749081178688,\n",
              "   139749081178400,\n",
              "   139749081180632,\n",
              "   139749081178328,\n",
              "   139749081179192,\n",
              "   139749081180344,\n",
              "   139749081181640],\n",
              "  'weight_decay': 0}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFS227GStoHa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}